Humans are very good at suppressing attentional distracters to focus on the cognitive or perceptually relevant objects in an environment. Psychologically, this is accomplished through schemas, or heuristics, that allow humans to rank and quickly select the most salient objects in an environment without a detailed evaluation of each object individually. These schemas can range from very simple (e.g., winner-take-all behavior) to quite complex (e.g., risk-aversion behavior) as they are learned and refined through experience. Machine perception systems require a full reevaluation of novel object or the same object in a different environment or context.
Traditional saliency-based ranking methods use perceptual features, such as visual pop out and spatial attention, as saliency measures to rank perceptual objects. When a new context or environment is encountered, traditional methods must reassess the salience of each object in the scene.
Rao et al. (U.S. Pat. No. 5,210,799), discloses a system and method for obtaining salient contours from two-dimensional images acquired by a sensor, which processes the two-dimensional images with an edge detector to produce edges from each of the images, link the edges into lists known as contours, compute a saliency value for each of the contours, rank the contours in decreasing order of saliency, and select certain ones of the ranked contours based on the requirements of a particular vision application. Rao's technique is a method for detecting and ranking visually salient objects using the neural mechanism of edge detection and spatial attention. However, such a method does not generalize across perceptual domains (e.g., auditory, somatosensory) or to cognitive domains (e.g., hypothesis generation and likelihood estimation).
More recent work in the scientific literature has focused on simple spike-timing based ranking models of visual saliency for ordering (see the List of Incorporated Cited Literature References, Literature Reference No. 5). As in the Rao et al. method described above, the spike-timing technique applies solely to the visual perceptual domain with the increased neural fidelity of a spiking network.
Thus, a continuing need exists for an approach that learns general saliency schemas and applies these schemas to novel environments or contexts without requiring perceptual features for saliency measures or ranking.